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Article

Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications

Department of Electronics Engineering, Eastern Barcelona School of Engineering (EEBE), Technical University of Catalonia–BarcelonaTech (UPC), 08019 Barcelona, Spain
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Academic Editor: Andrea Acquaviva
J. Low Power Electron. Appl. 2022, 12(3), 41; https://doi.org/10.3390/jlpea12030041
Received: 29 May 2022 / Revised: 28 June 2022 / Accepted: 9 July 2022 / Published: 18 July 2022
Electrical impedance tomography (EIT) is based on the physical principle of bioimpedance defined as the opposition that biological tissues exhibit to the flow of a rotating alternating electrical current. Consequently, here, we propose studying the characterization and classification of bioimpedance patterns based on EIT by measuring, on the forearm with eight electrodes in a non-invasive way, the potential drops resulting from the execution of six hand gestures. The starting point was the acquisition of bioimpedance patterns studied by means of principal component analysis (PCA), validated through the cross-validation technique, and classified using the k-nearest neighbor (kNN) classification algorithm. As a result, it is concluded that reduction and classification is feasible, with a sensitivity of 0.89 in the worst case, for each of the reduced bioimpedance patterns, leading to the following direct advantage: a reduction in the numbers of electrodes and electronics required. In this work, bioimpedance patterns were investigated for monitoring subjects’ mobility, where, generally, these solutions are based on a sensor system with moving parts that suffer from significant problems of wear, lack of adaptability to the patient, and lack of resolution. Whereas, the proposal implemented in this prototype, based on the so-called electrical impedance tomography, does not have these problems. View Full-Text
Keywords: bioimpedance; electrical impedance tomography; classification algorithm; principal component analysis; k-nearest neighbor; HMI interaction applications bioimpedance; electrical impedance tomography; classification algorithm; principal component analysis; k-nearest neighbor; HMI interaction applications
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MDPI and ACS Style

Vaquero-Gallardo, N.; Martínez-García, H. Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications. J. Low Power Electron. Appl. 2022, 12, 41. https://doi.org/10.3390/jlpea12030041

AMA Style

Vaquero-Gallardo N, Martínez-García H. Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications. Journal of Low Power Electronics and Applications. 2022; 12(3):41. https://doi.org/10.3390/jlpea12030041

Chicago/Turabian Style

Vaquero-Gallardo, Noelia, and Herminio Martínez-García. 2022. "Electrical Impedance Tomography for Hand Gesture Recognition for HMI Interaction Applications" Journal of Low Power Electronics and Applications 12, no. 3: 41. https://doi.org/10.3390/jlpea12030041

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